Video compression system

ABSTRACT

An improved video compression system in which the coefficient transform is sped up via folding transposition of coefficients into the token extraction phase. This is accomplished by filling a group of blocks coefficient buffers with 0&#39;s before the start of coefficient decoding, extracting the token from the bitstream and placing any extracted coefficient value which is non zero into the transposed order that best suits the target processor.

RELATED APPLICATIONS

This nonprovisional application claims domestic priority to prior provisional application Ser. No. 60/312,941, filed Aug. 16, 2001, and to provisional application Ser. No. 60/316,316, filed Aug. 31, 2001. Both provisional applications are incorporated herein by reference.

PRIOR ART

U.S. Pat. Nos. 6,178,205; 6,167,164; and 6,188,799 entitled “AN EFFICIENT REAL TIME ALGORITHM TO SIMULTANEOUSLY REDUCE BLOCKING AND RINGING ARTIFACTS OF COMPRESSED VIDEO” by Min-Cheol Hong, Chang Mo Yon and Young Man Park, assigned to Digital Media Research Lab IEEE 1999.

COMPUTER PROGRAM LISTING APPENDIX

The following computer software files, submitted herewith on one (1) CD-R compact disc, are incorporated by reference herein:

Format: IBM Compatible OS: Windows Directory of CD 08/06/02 01:53 p 12,710 blockmap.c 08/06/02 01:53 p 8,616 blockmapping.c 08/06/02 01:53 p 1,884 blockmapping.h 08/06/02 01:53 p 5,590 borders.c 08/06/02 01:53 p 4,796 cbitman.c 08/06/02 01:53 p 1,660 cbitman.h 08/06/02 01:53 p 6,203 cframew.c 08/06/02 01:53 p 1,510 cframew.h 08/06/02 01:53 p 10,181 cfrarray.c 08/06/02 01:53 p 1,376 cfrarray.h 08/06/02 01:53 p 4,066 codec_common.h 08/06/02 01:53 p 17,991 compdll.h 08/06/02 01:53 p 18,253 comp_globals.c 08/06/02 01:53 p 63,483 coptfunctions.c 08/06/02 01:53 p 8,194 coptfunctionsppc.c 08/06/02 01:53 p 91,862 cscanyuv.c 08/06/02 01:53 p 3,351 csystemdependant.c 08/06/02 01:53 p 31,760 cwmtfunctions.c 08/06/02 01:53 p 2,677 dct.h 08/06/02 01:53 p 36,952 dct_decode.c 08/06/02 01:53 p 24,698 ddecode.c 08/06/02 01:53 p 51,397 deblock.c 08/06/02 01:53 p 67,131 deblock_av.c 08/06/02 01:53 p 235,667 deblockopt.c 08/06/02 01:53 p 97,589 deblockwmtopt.c 08/06/02 01:53 p 34,241 decodeandrecon.c 08/06/02 01:53 p 34,050 dering.c 08/06/02 01:53 p 13,128 dering_av.c 08/06/02 01:53 p 74,338 deringopt.c 08/06/02 01:53 p 26,202 deringwmtopt.c 08/06/02 01:53 p 15,347 dframer.c 08/06/02 01:53 p 6,061 doptsystemdependant.c 08/06/02 01:53 p 5,014 dsystemdependant.c 08/06/02 01:53 p 15,146 encode.c 08/06/02 01:53 p 13,282 fdct.c 08/06/02 01:53 p 20,029 fdct_av.c 08/06/02 01:53 p 28,841 FilterOptMapca.c 08/06/02 01:53 p 7,873 framecopy.c 08/06/02 01:53 p 22,745 frameini.c 08/06/02 01:53 p 32,626 frarray.c 08/06/02 01:53 p 953 getinfo.c 08/06/02 01:53 p 11,819 huffman.c 08/06/02 01:53 p 3,890 huffman.h 08/06/02 01:53 p 52,378 hufftables.h 08/06/02 01:53 p 36,321 IDctMapCA.c 08/06/02 01:53 p 27,678 idctpart.c 08/06/02 01:53 p 28,483 idctpartppc.c 08/06/02 01:53 p 24,485 idct_av.c 08/06/02 01:53 p 33,873 loopfilter.c 08/06/02 01:53 p 18,704 loopf_asm.c 08/06/02 01:53 p 21,372 loopf_ppc.c 08/06/02 01:53 p 50,048 mcomp.c 08/06/02 01:53 p 3,721 mcomp.h 08/06/02 01:53 p 13,520 misc_common.c 08/06/02 01:53 p 1,729 misc_common.h 08/06/02 01:53 p 64,242 mmxidct.c 08/06/02 01:53 p 36,143 mmxrecon.c 08/06/02 01:53 p 19,616 mventropy.c 08/06/02 01:53 p 3,055 mventropy.h 08/06/02 01:53 p 30,648 newloopfppc.c 08/06/02 01:53 p 48,799 newlooptest_asm.c 08/06/02 01:53 p 43,844 optfunctionsppc.c 08/06/02 01:53 p 16,328 packvideo.c 08/06/02 01:53 p 16,487 pbdll.h 08/06/02 01:53 p 27,588 pbdll_if.c 08/06/02 01:53 p 10,769 pb_globals.c 08/06/02 01:53 p 8,881 pchost_mapca.h 08/06/02 01:53 p 32,370 pickmodes.c 08/06/02 01:53 p 6,171 postp.h 08/06/02 01:53 p 20,340 postproc.c 08/06/02 01:53 p 3,515 postproc_if.h 08/06/02 01:53 p 12,504 predictmotion.c 08/06/02 01:53 p 1,253 preproc.h 08/06/02 01:53 p 3,170 preprocfunctions.c 08/06/02 01:53 p 502 preprocconf.h 08/06/02 01:53 p 14,083 preprocglobals.c 08/06/02 01:53 p 2,087 preprocif.h 08/06/02 01:53 p 9,264 preprocif.c 08/06/02 01:53 p 49,951 quantize.c 08/06/02 01:53 p 3,835 quantize.h 08/06/02 01:53 p 4,743 quantindexmmx.c 08/06/02 01:53 p 4,489 quantindexppc.c 08/06/02 01:53 p 10,807 readtokensopt.c 08/06/02 01:53 p 15,450 reconstruct.c 08/06/02 01:53 p 2,549 reconstruct.h 08/06/02 01:53 p 1,703 resource.h 08/06/02 01:53 p 23,818 rowdiffscan.c 08/06/02 01:53 p 46,513 scale.c 08/06/02 01:53 p 21,804 scale_av.c 08/06/02 01:53 p 23,240 simpledeblock_asm.c 08/06/02 01:53 p 12,240 simpledeblocker.c 08/06/02 01:53 p 15,483 simpledeblockppc.c 08/06/02 01:53 p 769 StdAfx.h 08/06/02 01:53 p 1,661 systemdependant.h 08/06/02 01:53 p 4,235 timer.c 08/06/02 01:53 p 19,815 tokenize.c 08/06/02 01:53 p 16,560 transform.c 08/06/02 01:53 p 17,641 unpack.c 08/06/02 01:53 p 18,895 unpackvideo.c 08/06/02 01:53 p 30,389 unpackvideoppc.c 08/06/02 01:53 p 4,738 uoptsystemdependant.c 08/06/02 01:53 p 46,884 vfwcomp.c 08/06/02 01:53 p 29,958 vfwcomp_if.c 08/06/02 01:53 p 11,797 vp31dtest.c 08/06/02 01:53 p 9,675 vp31dxv.c 08/06/02 01:53 p 14,900 vp31etest.c 08/06/02 01:53 p 2,334 vp3d.h 08/06/02 01:53 p 134 vp3eprefix.h 08/06/02 01:53 p 10,840 vputil.c 08/06/02 01:53 p 13,669 vputilasm.c 08/06/02 01:53 p 1,698 vputil_if.h 08/06/02 01:53 p 64,250 wmtidct.c 08/06/02 01:53 p 8,248 wmtrecon.c 08/06/02 01:53 p 861 xprintf.h 116 File(s) 2,485,729 bytes 0 bytes free

BACKGROUND AND GENERAL DESCRIPTION

This application specifically relates to a method and apparatus for reducing the decode complexity of two dimensional inverse transforms on a vector process.

A typical digital video decoding system involves the following steps (among others).

For each block in a frame:

-   -   A) Extract quantized transform coefficients from the compressed         bit-stream     -   B) Perform inverse quantization to reconstruct the transform         coefficients     -   C) Perform an inverse transform (typically an IDCT) on the         coefficients     -   D) Add the resultant values to a block predictor     -   E) Output the block results

The 2-dimensional inverse transform functions typically take a large portion of the time to decode a frame due to their complexity.

The invention described here attempts to reduce the decoder complexity on vector processing machines that are capable of doing the same operation to multiple values stored sequentially in a machine's registers by lowering the complexity of the 2 dimensional transform.

A 2-dimensional separable inverse transform performed on a block typically involves performing the following steps:

-   -   a) For each row of the block:         -   Perform the same 1-dimensional inverse transform on the             transform coefficients.     -   b) For each column of the block column (resulting from (a)):         -   Perform the same 1-dimensional inverse transform on the             transform coefficients.

Since the 1-dimensional inverse transform usually involves performing exactly the same operations on a number of rows or columns in the block, vector processors are often used to reduce the decoding time. This is typically accomplished by filing vector processing registers with a value from each of N rows in the block (see diagram). The operations of the inverse transform are then performed on the N rows in parallel. And then the vector processing registers are filled with values from each of the N columns in the block and the inverse transform is then performed on the N columns in parallel.

In order to fill the vector processing registers quickly with different values from each row a programmer typically has two options:

-   -   a) Transpose the coefficients so that the transform coefficients         appear in the order that matches the vector processor and load         them directly into the registers.     -   b) Fill the vector registers one value at a time with the         coefficient data.

Choice (a) requires numerous operations to perform the transpose and choice (b) requires numerous bit-mask AND/OR operations to place each coefficient into the register.

SUMMARY OF THE INVENTION

This invention attempts to address these issues. To do so:

-   -   a) Fill an entire frame's coefficient buffers with 0 before the         start of coefficient decoding.     -   b) Extract the transform coefficient for a token from the         bit-stream.     -   c) If the coefficient value is non-zero place it in the         transposed order that best suits the target vector processor.

This works better than performing the transpose or filling the vector registers one at a time as part of the inverse transform because there are typically many more zero than non-zero coefficients. As such we make only a few positional changes, and avoid doing one full transpose altogether.

Additional savings are achieved by an embodiment of this technique whereby the same coefficient for every block in the image is encoded in the bit-stream before moving on to the next coefficient. In this way, one look up can be performed for each coefficient in the transform to determine where to place the transform coefficient.

Since each processor has vector registers with different cardinality it is necessary to place the coefficients in the order that best suits the processor being used.

The specific embodiment uses an IDCT transform but the technique is equally applicable to any separable 2-dimensional transform, for example, the discrete wavelet transform or the generalized orthogonal transform.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the embodiment.

FIG. 2 is an overview block diagram of the embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENT

The invention includes enhanced video processing and compression and is further described hereinafter.

The encoder uses a motion estimator, block based 8×8 Discrete Cosine Transform (DCT), a quantizer, a variable length encoder, and a loop filter for smoothing block edges in the reconstruction buffer. The decoder uses a variable length decoder and inverse quantizer, a motion compensator and a loop filter for smoothing block edges.

Two separate image artifacts are produced as a result of the quantization step. A blocking artifact is produced when quantization of the DCT coefficients in adjacent blocks produces pixel values on the shared block edge that differ on either side of the edge by a greater amount than in the original image. A ringing or mosquito artifact results from the quantization of higher frequency components of the transform around strong edges in the image. This means that the transform basis vectors do not reinforce and cancel correctly, producing edges in the reconstruction near to the strong edge that were not present in the original image.

The current invention embodies two separate but dependent filters that attempt to remove these image artifacts in a manner that is low on decoder complexity:

-   -   a) a de-blocking filter that reduces the blocking artifacts         described above     -   b) an edge-enhancement and de-ringing filter that attempts to         reduce the mosquito noise or ringing artifacts at the same time         it sharpens real text images.

A block diagram of the vectorized de-blocker is shown in FIG. 2.

The de-blocking filter of FIG. 2 works as follows:

-   -   Define an array, BlockSadArray, that contains one entry for each         8×8 block in the image. Initialize each entry of BlockSadArray         to the value 0.     -   For every border between two 8×8 blocks of the image:         -   At each of the eight positions along the border, examine the             10 pixels that lie in a direction perpendicular to the             border, five from each block on either side (see diagram).             Label these 10 pixels x0 . . . x4 and x5 . . . x9,             respectively.         -   Computer the following sums at each of the eight positions             on the border:

${Side1SAD} = {\sum\limits_{i + 1}^{4}{{abs}\left( {x_{i} - x_{i - 1}} \right)}}$ ${Side2SAD} = {\sum\limits_{i + 5}^{9}{{abs}\left( {x_{i} - x_{i - 1}} \right)}}$

-   -   Add Side1Sad to the entry in BlockSadArray corresponding to the         block that contained pixels x0 . . . x4 and Side2Sad to the         entry in BlockSadArray corresponding to the block that contained         pixels x5 . . . x9 (Note: BlockSadArray is used as input to the         de-ringing filter).     -   Define three threshold values T1, T2, T3 that are computed as a         function of the quantization level.

If the following condition is met: Side1Sad<3*T1/2 and Side2Sad<3*T2/2 and Abs(x4−x5)<T3, then replace values x1 to x8 with low pass filtered values x1 to x8 as follows: x1′=(x0+x0+x0+x1*2+x2+x3+x4+4)/8 x2′=(x0+x0+x1+x2*2+x3+x4+x5+4)/8 x3′=(x0+x1+x2+x3*2+x4+x5+x6+4)/8 x4′=(x1+x2+x3+x4*2+x5+x6+x7+4)/8 x5′=(x2+x3+x4+x5*2+x6+x7+x8+4)/8 x6′=(x3+x4+x5+x6*2+x7+x8+x9+4)/8 x7′=(x4+x5+x6+x7*2+x8+x9+x9+4)/8 x8′=(x5+x6+x7+x8*2+x9+x9+x9+4)/8

The invention encompasses the following novelty:

-   -   1: The decision as to whether or not to perform the de-blocking         operation can easily be computed for several positions at a time         using a vector processor (such as Intel Corporation Inc.'s MMX         architecture) with very few operations.     -   2: The filter applied itself is much simpler than most and         requires far fewer operations than those found in the prior art.

b) The edge enhancement and de-ringing filter works as follows:

-   -   For each 8×8 block in the image:         -   Let BSAV be the BlockSadArray value corresponding to the             current block.         -   Define three threshold values Medium Thresh, High Thresh,             Highest Thresh that are computed as a function of the             quantization level such that Highest         -   Thresh>High Thresh>Medium Thresh         -   IF (BSAV>Highest Thresh)             -   Apply the strong de-ringing filter in place to the block                 (described below) multiple times (based on how high BSAV                 is)         -   ELSE IF (BSAV>High Thresh)             -   Apply the strong de-ringing filter to the block                 (described below)         -   ELSE IF (BSAV>Medium Thresh)             -   Apply the weak de-ringing filter to the block (described                 below).

The strong de-ringing filter works as follows:

Calculate a maximum blurring modifier (HighModifier) and maximum sharpening modifier (LowModifier) by looking up a value based upon the level of quantization applied to the coefficients.

For the j^(th) pixel in the i^(th) image row, P_(i,j), do the following:

-   -   Calculate the following four values:         M₀=DeringModifier(P_(i,j), P_(i−1,j))         M₁=DeringModifier(P_(i,j), P_(i+1,j))         M₂=DeringModifier(P_(i,j), P_(i,1−j))         M₃=DeringModifier(P_(i,j), P_(i,−j+1))

Where DeringModifier(X,&) is defined as follows:

-   -   Value=Constant+QuantizationLevel−Abs(X−Y)     -   IF (Value<LowModifier)         -   Value=LowModifier     -   IF (Value>HighModifier)         -   Value=HighModifier

Replace Pixel P_(i,j) with P_(i,j)′ computed as follows: P _(i,j)′=(M ₀ *P _(i−1j) +M ₁ *P _(i+1j) +M ₂ *P _(ij−1) +M ₃ *P _(i,j+1)+(128−(M ₀ +M ₁ +M ₂ +M ₃)*P _(i,j))/128

The deblocker uses simple linear calculations and one dimensional filters to remove deblocking artifacts and to gather information used by the deringer filter to determine how many iterations to apply its filter. The deringing filter encompasses a pixel to pixel spatially adaptive filter that can both blur and sharpen. It does so by collecting the difference between neighboring pixels into a two dimensional array, applying a simple function to this array and then using the result as a convolution kernel. Since the function can produce both positive and negative tapes, the filter can perform both deringing and sharpening.

The Weak de-ringing filter works exactly the same as above except that the High and Low Modifiers are smaller magnitude numbers and the DeringModifier Function works as follows:

-   -   Value=Constant+QuantizationLevel−2*Abs(X−Y)     -   IF (Value<LowModifier)         -   Value=LowModifier     -   IF (Value>HighModifier)         -   Value=HighModifier

FIG. 1 describes the simplified process by which the 2d orthogonal transform works. The basic process is as follows: First initialize all of the coefficients for every block in a group of blocks to 0. Then determine the position of the best place in memory to place the first coefficient given the vector processor. Next extract the first coefficient for each block in a group of blocks, and write any non 0 coefficient to the appropriate location. Then determine the position of the best place in memory to place the second coefficient given the vector processor. Next extract the second coefficient for each block in a group of blocks, and write any non 0 coefficient to the appropriate location. Repeat this same process for each of the subsequent coefficients.

Enclosed with this application is a CD-Rom with the preferred embodiment illustrated by providing a listing in Source Code of the method, system and steps of this invention. Following a reading of said CD-Rom, the invention herein is again summarized.

The novelty of this approach in comparison to others found in prior art includes:

-   -   1: The selection of different kernel modifiers based upon the         difference between the target pixel and its neighbors which is         an extremely simple and solely integer based linear calculation         that may be efficiently implemented on a vector processor.     -   2: The ability of the filter to both sharpen real image edges         and blur away ringing artifacts and mosquito noise.     -   3: The usages of completely different de-ringing functions based         upon sum absolute difference value information gathered by the         de-blocker.     -   4: The ability to run the de-ringing functions iteratively based         on the BlockSad value for the block.

In summary, the following table identifies some of the advantages, features and benefits of this invention.

Feature Description Shar- A spatially adaptive kernel filter in which the kernel modifiers pen/De- are determined by applying a non-continuous function on the ringing pixel's neighborhood. The filter is capable of both sharpening Filter and blurring depending on the neighborhood. Different functions and number of iterations are used based upon the local variance measures determined by the deblocking filter and the quantization level used in building the block. Simpli- An extremely simple 1 dimensional kernel filter is applied across fied De- block boundaries that differs based upon the variance across the blocker block boundary and within the block border. The total amount adjusted is determined by the quantization level. MV The selection process of a new motion vector is ties to the cost cost of transmitting that specific mv (a higher value costs more). In addition the choice to use a mv is tied to the specific quantization level of the frame. Error metrics that measured error by performing a fdct and estimating the number of bits to achieve perceptual losslessness (defined via several papers) is employed. This measure was used to keep all of the costs in bits and could thus properly weight the cost of a new mv against the benefits. Iterative An iterative process is used for motion vector selection that in MV the first pass would mark all of the blocks as having a similar adjust- enough mv to use the first mv encountered in the neighborhood ment and in the second pass would pick a best mv for that neighborhood. Datarate A datarate control stepping mechanism is used that does not control allow any frame's quantization level to go above a certain using a level until an internal model of the player's buffer is model beyond a certain point, at which point a new maximum level of of the quantization is chosen. This same model of the player's buffer players is used to determine whether or not to drop frames (now with a buffer dampened threshold on the buffer overrun). We also use the model to decide when to undershoot the datarate to improve buffer fullness. The model is used to decide when to perform internal spatial resampling. Datarate Selection of the quantizer is now performed after all of the Control modes are chosen. A function based on an error metric for the Using frame which equals the sum of the errors for each selected block Error and the total number of blocks to be coded is used to pick a quantizer. Auto The keyframing choice algorithm was made to take into account Key two different factors: the percentage of the background that Framing stays exactly the same, and the percentage improvement in error score we get by using last frame predictors. DCT Novel Optimization Technique. Since the typical block has very Coef- few non zero coefficients, these coefficients are placed in trans- ficient posed position as we read them off the bitstream rather than Arrang- doing the transpose as part of the idct. The position these ement are placed differs based upon the specific processor. Quanti- Baseline set of values for mid range quality not best quality. For zation higher Q the table value = Q multilier * baseline value. Strategy For lower Q the table value is interpolated between the baseline value and the minimum allowed value. Round- Basically instead of rounding in a conventional way, e.g., 0.5– ing and 1.5 = >1 the rounding behavior is changed to reflect the ZeroBin distribution of samples. Because there are a lot more samples in Strategy the range 0.5–1.0 than in the range 1.0–1.5, the mean value for the range 0.5–1.5 the is less than 1. To counter this the rounding behavior is changed. For example, one might say that 0.65– 1.65 = >1.0. The aim here is to insure that the output value after inverse quantization is close to the mean of the samples that have ended up in that bin (and hence to minimize the mean square error). Where this gets complicated is around zero. Effectively the zero-bin size is increased somewhat. This is a mixed blessing. Increasing the zero-bin size too much can cause loss of sharpness (as observed in VP3.2) but it also has a profound effect on the output bit rate for a given Q setting. Thus at a fixed “bit rate” increasing the zero-bin size results in a lower average Q and hence more accurate coding of non-zero values. In VP4 these trade offs of rounding factor and zero bin size are adjusted according to the current Q value. At low Q (high quality) a smaller zero bin size and more conventional rounding is preferred, to insure sharpness. At higher Q, there is a shift towards truncation and a larger zero-bin size. For example one gets better results with a Q multiplier of 3.0 and a zero-bin size of +/−0.9 than with a Q multiplier of 5.0 and a zero bin size of +/−0.65. Motion Rather than applying a typical reconstruction loop filter a de- Predic- blocking filter is applied only to the motion predictor of blocks tion that cross a block boundary. This gets around the problem with Block accumulating dct errors and improves the prediction De- of the block. block- ing Token All coefficients for a block are transmitted in order, but the Order tokens used also account for some information about blocks that Trans- have yet to be transmitted. mittal Updates Context For modes, MVs and display fragments different token set Based probabilities are chosen based upon the information transmitted Entropy nearby. For modes and MVs a 2 dimensional context model with additional variables (like frame type) that blends these 3 items together into a set of probability distributions is used. For DC and AC coefficients the token probability sets are determined by a function of what the coefficient is, what plane (Y, U, or V) it came from and a predetermined frame type. A more complex model of the context and probabilities that makes use of the neighboring blocks frame type and position within the block is used to select between probability sets. Improv- The same block in subsequent frames is now guaranteed to keep ed re- getting a chance for update until no further coefficients are covery generated for that block, irregardless of whether it is marked block as background material. This helps avoid staleness issues. strategy Datarate As a final trap for huge datarate miscalculations the code is now Miscal- capable of restarting the compression of a frame if there were cula- a large error. This same technique is used to redo a tions section in the standalone lagged compressor. Post The manner in which we use a calculation based on bits per mhz Process- is used to determine between postprocessing mechanisms is new. ing Choices

Having described this invention with regard to specific embodiments, it is to be understood that the description is not meant as a limitation since further modifications and variations may be apparent or may suggest themselves to those skilled in the art. It is intended that the present application cover all such modifications and variations as fall within the scope of the appended claims. 

1. An encoder for compressing digital video signals comprising: a motion estimator; a block based discrete cosine transform system connected to said motion estimator; a quantizer connected to said discrete transform system, said quantizer producing separate image artifacts; a variable length encoder connected to said quantizer; a reconstruction buffer connected to said variable length encoder; and a loop filter connected to said reconstruction buffer for smoothing block edges therein, wherein said quantizer includes separate but functionally related filters which remove said separate image artifacts, said separate but functionally related filters including: a de-blocking filter reducing blocking artifacts which are produced by said quantizer; and an edge-enhancement and de-ringing filter to reduce ringing artifacts produced by said quantizer, said edge-enhancement and de-ringing filter sharpening said text images.
 2. A system of separate but functionally related filters which remove separate image artifacts left over from a video compression and decompression process, comprising: a de-blocking filter reducing blocking artifacts which are produced by a quantizer in a reconstruction frame; and an edge-enhancement and deranging filter that reduces ringing artifacts produced by the quantizer, said edge-enhancement and de-ringing filter sharpening said images, wherein one of said filters utilizes information determined by the other of said filters.
 3. A system according to claim 2, wherein said edge-enhancement filter utilizes information determined by said de-blocking filter, said utilized information comprising a block's adaptive strength value determined by said de-blocking filter.
 4. A system according to claim 2, wherein said de-blocking filter utilizes a value calculated by said edge-enhancement filter.
 5. An improved video decompression process comprising the steps of: filling substantially all of a video fiarne's coefficient buffers with 0's before starting coefficient decoding; extracting a coefficient of a transform for a token from a bitstream in zig zag order; placing coefficient values which are non-zero into a ordered coefficient buffer for case of use in an inverse transform, the order of the non-zero coefficient values dependent upon a type of processor that will be reconstructing the video frame.
 6. An improved video decompression process according to claim 5, wherein the order of the non-zero coefficient values comprises one of raster order, transposed order, or transposed raster order.
 7. An improved decompression process according to claim 6, wherein the order of the non-zero-coefficients is a transposed order, wherein an additional transpose is not required in order to speed up vector processing.
 8. An improved video decompression process according to claim 6, wherein the order of the non-zero coefficient values is a transposed raster order determined by the type of processor used, further comprising the step of minimizing a number of operations of the decompression process by localizing transpositions.
 9. An improved video decompression process according to claim 8, wherein if the processor is not a vector processor, the order of the non-zero coefficient values is a raster order.
 10. An improved video decompression process according to claim 8, wherein if the processor is a 2-element vector processor, the order of the non-zero coefficient values is a raster order with each 2×2 block transposed.
 11. An improved video decompression process according to claim 8, wherein if the processor is 4-element vector processor, the order of the non-zero coefficient values is a raster order with each 4×4 block transposed.
 12. An improved video decompression process according to claim 8, wherein if the processor is an 8-element vector processor, the order of the non-zero coefficient values is a raster order with each 8×8 block transposed.
 13. An improved video decompression process according to claim 8, wherein if the processor is an n-element vector processor, the order of the non-zero coefficient values is a raster order with each n×n block transposed.
 14. An improved video decompression process according to claim 5, further comprising the steps of: performing an inverse transform on the coefficient buffer to convert the coefficients to one or more of pixel values or error signal values; and reconstructing the image from a predictor block and said pixel values or error signal values. 